Area Under The Curve Research Articles

Enhancing type 2 diabetes mellitus prediction by integrating metabolomics and tree-based boosting approaches

BackgroundType 2 diabetes mellitus (T2DM) is a global health problem characterized by insulin resistance and hyperglycemia. Early detection and accurate prediction of T2DM is crucial for effective management and prevention. This study explores the integration of machine learning (ML) and explainable artificial intelligence (XAI) approaches based on metabolomics panel data to identify biomarkers and develop predictive models for T2DM.MethodsMetabolomics data from T2DM (n = 31) and healthy controls (n = 34) were analyzed for biomarker discovery (mostly amino acids, fatty acids, and purines) and T2DM prediction. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) regression to enhance the model’s accuracy and interpretability. Advanced three tree-based ML algorithms (KTBoost: Kernel-Tree Boosting; XGBoost: eXtreme Gradient Boosting; NGBoost: Natural Gradient Boosting) were employed to predict T2DM using these biomarkers. The SHapley Additive exPlanations (SHAP) method was used to explain the effects of metabolomics biomarkers on the prediction of the model.ResultsThe study identified multiple metabolites associated with T2DM, where LASSO feature selection highlighted important biomarkers. KTBoost [Accuracy: 0.938; CI: (0.880-0.997), Sensitivity: 0.971; CI: (0.847-0.999), Area under the Curve (AUC): 0.965; CI: (0.937-0.994)] demonstrated its effectiveness in using complex metabolomics data for T2DM prediction and achieved better performance than other models. According to KTBoost’s SHAP, high levels of phenylactate (pla) and taurine metabolites, as well as low concentrations of cysteine, laspartate, and lcysteate, are strongly associated with the presence of T2DM.ConclusionThe integration of metabolomics profiling and XAI offers a promising approach to predicting T2DM. The use of tree-based algorithms, in particular KTBoost, provides a robust framework for analyzing complex datasets and improves the prediction accuracy of T2DM onset. Future research should focus on validating these biomarkers and models in larger, more diverse populations to solidify their clinical utility.

Personal and Clinical Predictors of Voice Therapy Outcomes: A Machine Learning Analysis Using the Voice Handicap Index

In this study, we examine the predictive factors influencing the outcomes of voice treatment in patients with voice-related disorders, using the voice handicap index (VHI) as a key assessment tool. By analyzing various personal habits and clinical variables, we identify the primary factors associated with changes when comparing VHI scores before and after voice treatment. For this research, we employed binomial logistic regression, random forest (RF), and a multilayer perceptron (MLP) model to evaluate the effectiveness of voice treatment. The findings reveal that gender (with female patients showing greater improvements in VHI scores compared to male patients), surgical history, voice use status, and voice training status are significant predictors of therapy outcomes. The MLP model demonstrated high accuracy, sensitivity, and specificity, with an area under the curve (AUC) value of 0.87 indicating its potential as a valuable clinical predictive tool; however, the model’s relatively low specificity suggests the need for further refinement to enhance its predictive accuracy. The results of this study provide valuable insights for clinicians and speech–language pathologists in developing personalized treatment strategies to optimize the effectiveness of voice therapy. Future research should prioritize the validation of these findings in larger and more diverse population samples. Furthermore, it is essential to explore additional predictive variables in order to enhance the model’s accuracy across different types of voice disorders.

Preoperative Prediction of Perineural Invasion in Pancreatic Ductal Adenocarcinoma Using Machine Learning Radiomics Based on Contrast-Enhanced CT Imaging.

The objective of the study is to assess the clinical value of machine learning radiomics based on contrast-enhanced computed tomography (CECT) images in preoperative prediction of perineural invasion (PNI) status in pancreatic ductal adenocarcinoma (PDAC). A total of 143 patients with PDAC were enrolled in this retrospective study (training group, n = 100; test group, n = 43). Radiomics features were extracted from CECT images and selected by the Mann-Whitney U-test, Pearson correlation coefficient, and least absolute shrinkage and selection operator (LASSO). The logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and decision tree (DT) algorithms were trained to build radiomics models by radiomic features. Multivariate logistic regression was employed to identify independent predictors and establish clinical models. A combined model was constructed by integrating clinical and radiomics features. Model performances were assessed by receiver operating characteristic curves (ROCs) and decision curve analyses (DCAs). A total of 788 radiomics features were extracted from CECT images, of which 14 were identified as significant through the three-step selection process. Among the machine learning models, the SVM radiomics model exhibited the highest predictive performance in the test group, achieving an area under the curve (AUC) of 0.831, accuracy of 0.698, sensitivity of 0.677, and specificity of 0.750. After logistic regression screening, the clinical model was established using carbohydrate antigen 19-9 (CA199) levels as one independent predictor. In the test group, the clinical model demonstrated an AUC of 0.644, accuracy of 0.744, sensitivity of 0.871, and specificity of 0.417. The combined model showed improved performance compared to both the clinical and radiomics models in the test group, with an AUC of 0.844, accuracy of 0.767, sensitivity of 0.806, and specificity of 0.667. Subsequently, DCA of the combined model indicated optimal clinical value for predicting PNI status. Machine learning radiomics models can accurately predict PNI status in patients with pancreatic ductal adenocarcinoma. The combined model, which incorporates clinical and radiomics features, enhances preoperative diagnostic performance and aids in the selection of treatment methods.

Image-based deep learning in diagnosing mycoplasma pneumonia on pediatric chest X-rays.

Correctly diagnosing and accurately distinguishing mycoplasma pneumonia in children has consistently posed a challenge in clinical practice, as it can directly impact the prognosis of affected children. To address this issue, we analyzed chest X-rays (CXR) using various deep learning models to diagnose pediatric mycoplasma pneumonia. We collected 578 cases of children with mycoplasma infection and 191 cases of children with virus infection, with available CXR sets. Three deep convolutional neural networks (ResNet50, DenseNet121, and EfficientNetv2-S) were used to distinguish mycoplasma pneumonia from viral pneumonia based on CXR. Accuracy, area under the curve (AUC), sensitivity, and specificity were used to evaluate the performance of the model. Visualization was also achieved through the use of Class Activation Mapping (CAM), providing more transparent and interpretable classification results. Of the three models evaluated, ResNet50 outperformed the others. Pretrained with the ZhangLabData dataset, the ResNet50 model achieved 80.00% accuracy in the validation set. The model also showed robustness in two test sets, with accuracy of 82.65 and 83.27%, and AUC values of 0.822 and 0.758. In the test results using ImageNet pre-training weights, the accuracy of the ResNet50 model in the validation set was 80.00%; the accuracy in the two test sets was 81.63 and 62.91%; and the corresponding AUC values were 0.851 and 0.776. The sensitivity values were 0.884 and 0.595, and the specificity values were 0.655 and 0.814. This study demonstrates that deep convolutional networks utilizing transfer learning are effective in detecting mycoplasma pneumonia based on chest X-rays (CXR). This suggests that, in the near future, such computer-aided detection approaches can be employed for the early screening of pneumonia pathogens. This has significant clinical implications for the rapid diagnosis and appropriate medical intervention of pneumonia, potentially enhancing the prognosis for affected children.

Preoperative Predictors of Recurrent Tricuspid Regurgitation After Annuloplasty: Insights into the Role of 3D Echocardiography

Background: While tricuspid annuloplasty (TAP) is an effective treatment option for tricuspid regurgitation (TR), understanding the echocardiographic factors contributing to recurrent TR can help in developing more effective preventive measures to reduce the rate of recurrent TR after TAP. Methods: This study was designed as a prospective observational cohort study to investigate factors contributing to recurrent TR following surgical tricuspid valve (TV) repair in patients with moderate or severe functional TR caused by left heart valvular disease, with severe mitral regurgitation as the dominant pathology. The study included 66 patients who underwent preoperative two-dimensional (2D) and three-dimensional (3D) echocardiographic assessments. Patients were divided into two groups based on TAP outcomes: the effective TAP group and the recurrent TR group. Results: The analysis revealed that 3D-derived both septal–lateral diastolic and systolic tricuspid annulus (TA) diameter (odds ratio (OR) 1.77; 95% confidence interval (CI) 1.17–2.68 and OR 1.62; 95% CI 1.14–2.29, respectively), and major axis diastolic TA diameter (OR 1.59; 95% CI 1.15–2.2) had the highest OR among all echocardiographic parameters. The further univariate analysis of predefined echocardiographic values unveiled that the combined effect of heightened 3D-measured TA major axis diastolic diameter and increased right ventricle (RV) basal diameter exhibited the highest OR at 12.8 (95% CI 2.3–72.8) for a recurrent TR. Using ROC analysis, diastolic major axis (area under the curve (AUC) 0.848; cut-off 48.5 mm), septal-lateral systolic (AUC 0.840; cut-off 43.5 mm) and diastolic (AUC 0.840; cut-off 46.5 mm) TA diameter demonstrated the highest predictive value for recurrent TR from all TV parameters. Conclusions: Recurrent moderate or severe TR after TAP is associated with preoperative TA size, right atrium and RV geometry, but not with changes of RV function. The predictive capacity of 2D-assessed echocardiographic parameters was found to be lower when compared to their corresponding 3D parameters.

The minimally clinically important difference in the 2-minute walk test for people in the subacute phase after a stroke

ABSTRACT Background The 2-Minute Walk Test (2MWT) is a simple and reliable test used by clinicians to assess gait function in people with stroke (pwST). No studies established the minimal clinically important difference (MCID) of the 2MWT. Objective To determine the MCID of the 2MWT in subacute pwST using data from a longitudinal cohort study. Methods PwST within 180 days of stroke onset were recruited from the Italian National Health System (NHS) rehabilitation services across the country. Participants underwent physical therapy to improve balance and gait according to their specific needs. The 2MWT was used to assess gait performance at the beginning (T0) and after a minimum of 10 rehabilitation sessions (T1). The Global Perceived Effect (GPE), Activities-specific Balance Confidence Scale (ABC) and the ABC_gait were used to assess balance confidence and the perceived effect of the intervention at T1. Results 51 pwST (69 ± 12 years; 66.7% males) were included in the analysis. Statistically significant improvements were observed in 2MWT, ABC, and ABC_gait scores after rehabilitation using the Wilcoxon signed-rank test. Using an anchor-based approach the receiver operating characteristic (ROC) curves were calculated to establish the MCID. The MCID of the 2MWT was 31 meters with an Area under the curve (AUC) = 0.74 [0.60–0.89], a specificity of 71% and a sensitivity of 63%. Conclusions An improvement of 31 meters on the 2MWT can be considered clinically significant in subacute pwST undergoing rehabilitation. This study provides valuable insights for clinicians to assess walking performance in pwST and determine clinically meaningful changes post-rehabilitation.

The Power of WNT5A and FZD3 Gene Expression and Methylation Status in the Diagnosis–Treatment–Cause Triangle in Tension-Type Headache

DNA methylation is the epigenetic pathway controlling cellular gene expression. Methylation is a natural and cellular epigenetic mechanism for gene silencing. The fact that the genes that the cell decides to be silent do not speak or begin to speak may coincide with diseases. For explanatory evidence, changes at the DNA level can provide realistic information. Wnt/β-catenin signaling has an important role in the pain process. For this purpose, we investigated the relationship between clinical data, wingless-type MMTV integration site family, member 5A (WNT5A), and Frizzled Class Receptor 3 (FZD3) gene methylation and expression in a cohort of tension-type headache (TTH) patients (N = 130) and healthy control (N = 117) individuals. Comorbidities were evaluated. Methylation profiling was performed using Real-Time PCR with a TaqMan primer-probe. The diagnostic power (receiver operating characteristic—ROC) was determined according to the expression and methylation status. Ultimately, WNT5A was found to be upregulated and hypermethylated, and FZD3 was found to be upregulated and hypomethylated. Finally, the area under the curve (AUC) data for FZD3 upregulation (0.983) and hypomethylation (0.866) showed diagnostic values. WNT5A and FZD3 may contribute to the pathogenesis of the disease depending on their expression and methylation profile during the TTH process. At the same time, diagnostic powers have the potential to be a resource for early treatment and new therapeutic approaches.

Serum Anti-Aminoacyl-Transfer Ribonucleic Acid Synthetase Antibody Levels Are Involved in Rheumatoid Arthritis Complicated with Interstitial Lung Disease

Objectives: A common complication in patients with rheumatoid arthritis (RA) is interstitial lung disease (ILD). Antibodies (Abs) to anti-aminoacyl-transfer ribonucleic acid synthetase (ARS) are linked to ILD in patients with idiopathic inflammatory myopathies (IIM). There have been limited studies of anti-ARS Abs in RA. In this study, we examined anti-ARS Abs in ILD in patients with RA. Methods: Anti-ARS Abs in serum from patients with RA were measured. Results: There were higher anti-ARS Ab levels in RA patients with ILD (mean ± SDM, 16.3 ± 32.3 vs. 7.4 ± 7.0 (Index), p = 5.58 × 10−12), usual interstitial pneumonia (14.4 ± 24.4 vs. 7.4 ± 7.0 [Index], p = 3.14 × 10−12), and nonspecific interstitial pneumonia (17.9 ± 37.7 vs. 7.4 ± 7.0 (Index), p = 5.07 × 10−5) compared with patients without chronic lung disease. The area under the curve (AUC) of the receiver operating characteristic curve for anti-ARS Ab was too low to allow for discrimination among RA patients with/without chronic lung disease (0.608, 95% confidence interval (CI) 0.560–0.655, p = 8.69 × 10−6). Multiple logistic regression analyses of age, smoking status, anti-ARS Abs, as well as Steinbrocker stage generated an ARS-index with a high AUC value (0.707, 95%CI 0.662–0.752, p = 2.20 × 10−19). Conclusions: Anti-ARS Abs are related to ILD pathogenesis in RA and may be a biomarker for ILD.

Changes in soluble PD-L1 levels in patients with advanced non-small cell lung cancer

Abstract Soluble programmed death-1 ligand (sPD-L1) in the serum of non-small cell lung cancer (NSCLC) patients is a crucial factor in disease prognosis and an indicator for immunotherapy response. This study aimed to evaluate changes in sPD-L1 levels in advanced NSCLC patients. Between May 2018 and October 2022, 80 patients with advanced-stage NSCLC and 30 healthy volunteers participated in a prospective study. The findings revealed a significant rise in sPD-L1 concentration in the lung cancer group compared to the normal control group (1.08 vs. 0.42, p < 0.001). No apparent correlation was found between sPD-L1 concentration and membrane-bound programmed death-1 ligand (mPD-L1) expression. The optimal diagnostic threshold for sPD-L1 was set at 0.92 ng/mL, showing a sensitivity of 56.25% and specificity of 77.33%, with an area under the curve (AUC) of 0.743 (p = 0.001). Although increased sPD-L1 levels were prevalent in individuals with advanced age, prolonged disease duration, large tumor size, and finger clubbing, these associations did not reach statistical significance (p > 0.05). In conclusion, sPD-L1 levels significantly increased in advanced NSCLC patients compared to controls (p < 0.05), with no association observed with mPD-L1 (p = 0.304). The study suggests that sPD-L1 concentration can be a specific biomarker for guiding the diagnosis of advanced NSCLC, with a recommended cutoff value of 0.92 ng/mL, demonstrating a sensitivity of 56.25% and specificity of 77.33% (p = 0.001). Importantly, no apparent relationship was established between sPD-L1 levels and clinical or paraclinical characteristics in advanced NSCLC patients.

Comparing nSOFA, CRIB-II, and SNAPPE-II for predicting mortality and short-term morbidities in preterm infants ≤32 weeks gestation

Background Neonatal illness severity scores are not extensively studied for their ability to predict mortality or morbidity in preterm infants. The aim of this study was to compare the Neonatal Sequential Organ Failure Assessment (nSOFA), Clinical Risk Index for Babies-II (CRIB-II), and Score for Neonatal Acute Physiology with Perinatal extension-II (SNAPPE-II) for predicting mortality and short-term morbidities in preterm infants ≤32 weeks. Methods In this retrospective study, infants born in 2017–2018 with gestational age (GA) ≤32 weeks were evaluated. nSOFA, CRIB-II, and SNAPPE-II scores were calculated for each patient, and the ability of these scores to predict mortality and morbidities was compared. The morbidities were categorized as mod/sev bronchopulmonary dysplasia (BPD), necrotizing enterocolitis (NEC) requiring surgery, early-onset sepsis (EOS), late-onset sepsis (LOS), retinopathy of prematurity (ROP) requiring treatment, and severe intraventricular hemorrhage (IVH). Calculating the area under the curve (AUC) on receiver operating characteristic curves (ROC) analysis to predict and compare scoring systems’ accuracy. Results A total of 759 preterm infants were enrolled, of whom 88 deceased. The median nSOFA, CRIB-II, and SNAPPE-II scores were 2 (0, 3), 6 (4, 8), and 13 (5, 26), respectively. Compared with infants who survived, these three scores were significantly higher in those who deceased (p < 0.05). For predicting mortality, the AUC of the nSOFA, SNAPPE-II, and CRIB-II were 0.90, 0.82, and 0.79, respectively. The nSOFA scoring system had significantly higher AUC than CRIB-II and SNAPPE-II (p < 0.05). However, short-term morbidities were not strongly correlated with these three scoring systems. Conclusion In infants ≤32 weeks gestation, nSOFA scoring system is more valuable in predicting mortality than SNAPPE-II and CRIB-II. However, further studies are required to assess the predictive power of neonatal illness severity scores for morbidity.

Clinical efficacy of DSA-based features in predicting outcomes of acupuncture intervention on upper limb dysfunction following ischemic stroke

Background and objectivesThis study aimed to employ machine learning techniques to predict the clinical efficacy of acupuncture as an intervention for patients with upper limb motor dysfunction following ischemic stroke, as well as to assess its potential utility in clinical practice.MethodsMedical records and digital subtraction angiography (DSA) imaging data were collected from 735 ischemic stroke patients with upper limb motor dysfunction who were treated with standardized acupuncture at two hospitals. Following the initial screening, 314 patient datasets that met the inclusion criteria were selected. We applied three deep-learning algorithms (YOLOX, FasterRCNN, and TOOD) to develop the object detection model. Object detection results pertaining to the cerebral vessels were integrated into a clinical efficacy prediction model (random forest). This model aimed to classify patient responses to acupuncture treatment. Finally, the accuracies and discriminative capabilities of the prediction models were assessed.ResultsThe object detection model achieved an optimal recognition rate, The mean average precisions of YOLOX, TOOD, and FasterRCNN were 0.61, 0.7, and 0.68, respectively. The prediction accuracy of the clinical efficacy model reached 93.6%, with all three-treatment response classification area under the curves (AUCs) exceeding 0.95. Feature extraction using the prediction model highlighted the significant influence of various cerebral vascular stenosis sites within the internal carotid artery (ICA) on prediction outcomes. Specifically, the initial and C1 segments of the ICA had the highest predictive weights among all stenosis sites. Additionally, stenosis of the middle cerebral, anterior cerebral, and posterior cerebral arteries exerted a notable influence on the predictions. In contrast, the stenosis sites within the vertebral artery exhibited minimal impact on the model's predictive abilities.ConclusionsResults underscore the substantial predictive influence of each cerebral vascular stenosis site within the ICA, with the initial and C1 segments being pivotal predictors.

Deep learning radiomics based on contrast enhanced MRI for preoperatively predicting early recurrence in hepatocellular carcinoma after curative resection

PurposeTo explore the role of deep learning (DL) and radiomics-based integrated approach based on contrast enhanced magnetic resonance imaging (CEMRI) for predicting early recurrence (ER) in hepatocellular carcinoma (HCC) patients after curative resection.MethodsTotal 165 HCC patients (ER, n = 96 vs. non-early recurrence (NER), n = 69) were retrospectively collected and divided into a training cohort (n = 132) and a validation cohort (n = 33). From pretreatment CEMR images, a total of 3111 radiomics features were extracted, and radiomics models were constructed using five machine learning classifiers (logistic regression, support vector machine, k-nearest neighbor, extreme gradient Boosting, and multilayer perceptron). DL models were established via three variations of ResNet architecture. The clinical-radiological (CR), radiomics combined with clinical-radiological (RCR), and deep learning combined with RCR (DLRCR) models were constructed. Model discrimination, calibration, and clinical utilities were evaluated by receiver operating characteristic curve, calibration curve, and decision curve analysis, respectively. The best-performing model was compared with the widely used staging systems and preoperative prognostic indexes.ResultsThe RCR model (area under the curve (AUC): 0.841 and 0.811) and the optimal radiomics model (AUC: 0.839 and 0.804) achieved better performance than the CR model (AUC: 0.662 and 0.752) in the training and validation cohorts, respectively. The optimal DL model (AUC: 0.870 and 0.826) outperformed the radiomics model in the both cohorts. The DL, radiomics, and CR predictors (aspartate aminotransferase (AST) and tumor diameter) were combined to construct the DLRCR model. The DLRCR model presented the best performance over any model, yielding an AUC, an accuracy, a sensitivity, a specificity of 0.917, 0.886, 0.889, and 0.882 in the training cohort and of 0.844, 0.818, 0.800, and 0.846 in the validation cohort, respectively. The DLRCR model achieved better clinical utility compared to the clinical staging systems and prognostic indexes.ConclusionBoth radiomics and DL models derived from CEMRI can predict HCC recurrence, and DL and radiomics-based integrated approach can provide a more effective tool for the precise prediction of ER for HCC patients undergoing resection.

Perirenal Fat CT Radiomics-Based Survival Model for Upper Tract Urothelial Carcinoma: Integrating Texture Features with Clinical Predictors

Background: Upper tract urothelial carcinoma (UTUC) presents significant challenges in prognostication due to its rarity and complex anatomy. This study introduces a novel approach integrating perirenal fat (PRF) radiomics with clinical factors to enhance prognostic accuracy in UTUC. Methods: The study retrospectively analyzed 103 UTUC patients who underwent radical nephroureterectomy. PRF radiomics features were extracted from preoperative CT scans using a semi-automated segmentation method. Three prognostic models were developed: clinical, radiomics, and combined. Model performance was assessed using concordance index (C-index), time-dependent Area Under the Curve (AUC), and integrated Brier score. Results: The combined model demonstrated superior performance (C-index: 0.784, 95% CI: 0.707–0.861) compared to the radiomics (0.759, 95% CI: 0.678–0.840) and clinical (0.653, 95% CI: 0.547–0.759) models. Time-dependent AUC analysis revealed the radiomics model’s particular strength in short-term prognosis (12-month AUC: 0.9281), while the combined model excelled in long-term predictions (60-month AUC: 0.8403). Key PRF radiomics features showed stronger prognostic value than traditional clinical factors. Conclusions: Integration of PRF radiomics with clinical data significantly improves prognostic accuracy in UTUC. This approach offers a more nuanced analysis of the tumor microenvironment, potentially capturing early signs of tumor invasion not visible through conventional imaging. The semi-automated PRF segmentation method presents advantages in reproducibility and ease of use, facilitating potential clinical implementation.

Association Between Finger Plethysmographic Features and Impedance-Based Thoracic Fluid Content Measurement in a Lower Body Negative Pressure Model of Hemorrhagic Shock.

Timely identification of the need for lifesaving intervention in battlefield conditions may be improved through automated monitoring of the injured warfighter. Technologies that combine maximal noninvasive insight with minimal equipment footprint give the greatest opportunity for deployment at scale with inexperienced providers in forward areas. Finger photoplethysmography (PPG) signatures are associated with impending hemorrhagic shock but may be insufficient alone. Transthoracic impedance (TTI) monitoring is a complementary modality to PPG and able to identify volume loss and estimate functional cardiovascular parameters. We sought to understand how PPG features correlate with volume loss estimation from TTI during lower body negative pressure (LBNP) challenge. We hypothesized that features of the PPG waveform would correlate with thoracic fluid content (TFC) as measured by TTI. We obtained physiologic monitoring data from healthy adult subjects in LBNP hemorrhagic shock models after local Institutional Review Board and DoD Human Research Protection Office approval. Subjects were excluded for pregnancy, age >45 years, and conditions prohibitive of LBNP exposure. Subjects were instrumented with noninvasive sensors, including a finger PPG sensor and a TTI monitor. Subjects underwent a stepwise LBNP exposure program of -10 mmHg every 10 minutes and notified laboratory staff at first sign of near syncope, terminating the sequential program. TTI data were continuously streamed to a custom program written in MATLAB and time synchronized. To calculate PPG measures, we downsampled data to 250 Hz, screened, and parsed each beat. We featurized each beat to include a systolic, diastolic, and dicrotic notch peak, beat length and area under the curve (AUC), peak-to-peak systolic/diastolic interval, and leading/trailing slopes, all normalized to instantaneous heart rate. Thoracic fluid content was normalized to subjects' pre-LBNP baselines. We summarized all PPG features and the TFC using means (SD) generated as a subject average for each step. We used generalized estimating equation models to examine the relationship between TFC and PPG features while controlling for LBNP stage and subject. Thirty-two subjects were enrolled; 4 participants were excluded because of sensor malfunction. Twenty-eight subjects had a mean (SD) age of 25.11 (6.66) years. A total of 35.7% of subjects were female. Photoplethysmography analysis demonstrated a decreased systolic-diastolic peak interval, diastolic peak height, and beat AUC with decreased LBNP pressure. End-stage baseline normalized TFC showed an average decrease of 14.68% (±4.98%) (range: 7.54% to 27.69%). The strongest average correlations between stage TFC and PPG occurred in beat length (0.68) and normalized AUC (0.69). In generalized estimating equation models incorporating all stages, beat length, normalized AUC, and the systolic-diastolic interval were all significantly associated with time as a function of LBNP level (P < .001). Thoracic fluid content began decreasing at 12.8 (4.7) minutes, the normalized AUC decreased at 20.7 (7.2) minutes, the beat length decreased at 20.9 (7.0) minutes, and the systolic-diastolic time interval decreased at 30.6 (16.7) minutes. While both PPG features and impedance-based TFC trend congruently in the perishock state following LBNP exposure, peripheral pulse wave signals lag redistribution of thoracic fluid volume. Photoplethysmography features of beat length and normalized AUC may serve as a surrogate for TFC when direct thoracic sensing is not available.

Prognostic value of computed tomography-derived fractional flow reserve in patients with diabetes mellitus and unstable angina

BackgroundCoronary artery calcification is commonly found in patients with type 2 diabetes mellitus (T2DM), which may compromise the diagnostic accuracy of coronary computed tomography angiography (CTA). Computed tomography-derived fractional flow reserve (CT-FFR), which integrates coronary anatomy with functional assessment, holds the potential to become a powerful diagnostic tool for evaluating calcified lesions.ObjectiveWe aim to assess the prognostic value of CT-FFR for calcific lesions in patients with T2DM and unstable angina (UA).MethodsWe conducted a retrospective study involving 3,392 patients who were diagnosed with T2DM and UA who underwent coronary CTA, with at least one visible calcification site. Of those, 1,091 patients and 1,372 vessels were recommended by cardiovascular specialists and completed invasive coronary angiography (ICA) and invasive fractional flow reserve (FFR) measurements. Simultaneously, those patients also underwent CT-FFR measurements and were divided into two groups based on CT-FFR values: one group with CT-FFR > 0.80 and the other with CT-FFR ≤ 0.80. Demographics, clinical data, the diagnostic performance of CT-FFR, analysis of calcified lesions on CTA, and major adverse events during follow-up were recorded.ResultsThe diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the area under the curve (AUC) of CT-FFR were 84.8%, 84.6%, 85.1%, 84.7%, 85.0%, and 84.8%, respectively, per patient, and 82.2%, 80.3.2%, 81.8%, 79.7%, 81.1%, and 82.9% respectively, per vessel. For lesion and calcification characteristics, the degree of stenosis, lesion length, rate of bifurcation lesions, diffusive lesions, occlusion, calcium volume, and coronary artery calcification score (CACS) were significantly higher in the CT-FFR ≤ 0.8 group compared to the CT-FFR > 0.8 group. In contrast, the minimum cross-sectional area was smaller in the CT-FFR ≤ 0.8 group than in the CT-FFR > 0.8 group. Major adverse cardiovascular and cerebrovascular events (MACCE) at the 3-year follow-up was significantly higher in the CT-FFR ≤ 0.8 group compared to the CT-FFR > 0.8 group. The CT-FFR value is an independent predictor of MACCE at the 3-year follow-up.ConclusionCT-FFR demonstrated significant diagnostic performance using invasive FFR as the reference standard and proved to be an important predictive tool for assessing prognosis not only in calcified lesions but also in lesions with a CACS score of zero in patients with T2DM and UA. CT-FFR may serve as a valuable tool for guiding treatment decisions in these patients.Graphical abstract

Validity and reliability testing of the Shatri Sinulingga psychosomatic test (SSPT) questionnaire as a screening instrument for psychosomatic disorders in Indonesia

The incidence of psychosomatic disorders is increasing in Indonesia, and therefore screening instruments that are culturally appropriate for the Indonesian population are needed. The aim of this study was to assess the validity and reliability of the Shatri Sinulingga psychosomatic test (SSPT) questionnaire as a screening instrument for psychosomatic disorders in Indonesia. An analytic descriptive cross-sectional study divided into two stages (questionnaire formulation and distribution through the Psikosom.id application) was conducted in 2023. The validity test was carried out using the product moment technique, and Cronbach's alpha assessment was carried out to evaluate the reliability of the questionnaire. A total of 372 participants were included, with a mean age of 39.24 years old. The SSPT questionnaire scores had strong correlations with the scores of hospital anxiety and depression scale (HADS) (r=0.668; p&lt;0.001) and the Symptom Checklist-90 (SCL-90) (r=0.674; p&lt;0.001); and moderately correlated with the score of fatigue severity scale (FSS) (r=0.505; p&lt;0.001) and Pittsburgh Sleep Quality Index (PSQI) (r=0.492; p&lt;0.001). The correlation coefficient (r) value of each SSPT question ranged from 0.389–0.726, and all r higher than r table with an overall Cronbach’s alpha of 0.744. The SSPT questionnaire's strong predictive performance: had a sensitivity of 75.6% and specificity of 71.4% with an area under the curve (AUC) of 81% (95%CI: 76.6–85.4%; p&lt;0.001) to HADS; sensitivity of 80% and specificity of 58.6% with an AUC of 75.1% (95%CI: 70–80.2%; p&lt;0.001) to FSS; sensitivity of 64% and specificity of 67.5% with an AUC of 71.2% (95%CI: 65.4–77%; p&lt;0.001) to PSQI; and had sensitivity of 78% and specificity of 67.4% with an AUC of 80.9% (95%CI: 76.6–85.3%; p&lt;0.001) to SCL-90. The study highlights that the SSPT questionnaire is valid and reliable to be used as a screening instrument for psychosomatic disorders in Indonesia.

The Demographic and Clinical Characteristics, Prognostic Factors, and Survival Outcomes of Head and Neck Carcinosarcoma: A SEER Database Analysis

Background: Head and neck carcinosarcoma (HNCS) is a rare and highly aggressive malignancy with limited research, resulting in an incomplete understanding of disease progression and a lack of reliable prognostic tools. This study aimed to retrospectively analyze the clinical characteristics and outcomes of HNCS patients using data from the Surveillance, Epidemiology, and End Results (SEER) database and to develop a nomogram to predict overall survival (OS) and cancer-specific survival (CSS). Methods: Patients diagnosed with HNCS from 1975 to 2020 were identified in the SEER database. Univariate and multivariate Cox regression analyses were conducted to identify independent prognostic indicators, with the optimal model selected using the minimal Akaike Information Criterion (AIC). The identified prognostic factors were incorporated into nomograms to predict OS and CSS. Model performance was assessed using the concordance index (C-index), area under the curve (AUC), calibration curves, and decision curve analysis (DCA). Survival curves were generated using Kaplan–Meier analysis and compared via the log-rank test. Results: A total of 152 HNCS patients were included, with 108 assigned to the training cohort and 44 to the validation cohort in a 7:3 ratio. Prognostic factors including age, primary tumor site, marital status, radiotherapy, chemotherapy, tumor size, pathological grade, and tumor stage were incorporated into the nomogram models. The models demonstrated strong predictive performance, with C-index values for OS and CSS of 0.757 and 0.779 in the training group, and 0.777 and 0.776 in the validation group, respectively. AUC values for predicting 3-, 5-, and 10-year OS were 0.662, 0.713, and 0.761, and for CSS the values were 0.726, 0.703, and 0.693. Kaplan–Meier analysis indicated significantly improved survival for patients with lower risk scores. The 3-, 5-, and 10-year OS rates for the entire cohort were 54.1%, 45.6%, and 35.1%, respectively, and the CSS rates were 62.9%, 57.5%, and 52.2%, respectively. Conclusions: This study provides validated nomograms for predicting OS and CSS in HNCS patients, offering a reliable tool to support clinical decision-making for this challenging malignancy. These nomograms enhance the ability to predict patient prognosis and personalize treatment strategies.

Hypernatremia is associated with mortality in severe elderly sepsis patients.

To explore the relationship between hypernatremia and 28-day mortality in elderly sepsis patients. A total of 179 elderly patients (age ≥65 years) with elevated serum sodium admitted to the Department of Critical Care Medicine of Nanjing Hospital affiliated with Nanjing Medical University from September 2021 to September 2022 were included in this retrospective observational study. The clinical data of all patients were collected, and the patients were divided into septic group and nonseptic groups according to the Sepsis 3.0 definition. The clinical features, acute physiological and chronic health II score (APACHE II score), mechanical ventilation time, serum sodium value and duration of serum sodium elevation were compared between the two groups. ROC curves were drawn to evaluate the predictive value of each index on the prognosis of sepsis patients, and Kaplan‒Meier survival analysis was carried out on patients with different serum sodium peaks. (1) The changes in serum sodium within 48 hours after admission in the sepsis group were small and statistically significant compared with those in the nonsepsis group (P = 0.039); however, the serum sodium elevation duration was longer (P = 0.018). (2) Compared with nonseptic patients, the 7-day mortality of septic patients was higher (15.8 vs. 7.7, P<0.001). The 28-day mortality of septic patients was higher than that of nonseptic patients, but there was no significant difference between the two groups (P = 0.086). (3) The serum sodium level in the sepsis group was higher than that in the nonsepsis group on the 1st, 3rd, 5th and 7th days (P<0.001). There was no significant difference in mechanical ventilation time or duration of stay in the ICU between the two groups. (4) The ROC curve analysis showed that the peak value of serum sodium had predictive value for the prognosis severity of elderly patients with sepsis. The area under the curve (AUC) was 0.753, the 95% confidence interval (95% CI) was 0.639~0.867, and the best cut-off value was 154.9 mmol/L. (5) According to the best cut-off value of the serum sodium peak, the septic patients were divided into two groups: the peak value of serum sodium was ≥154.9 mmol/L (group A), and the peak value of serum sodium was <154.9 mmol/L (group B). Among them, the case fatality rate was higher at 7 days and 28 days when the peak value of serum sodium was ≥154.9 mmol/L (group A) (22.0% vs. 8.6%); the χ2 value was 35.379, P<0.05; 75.6% vs. 37.1%, χ2 = 14.21, P = 0.003). There was no significant difference in mechanical ventilation time or duration of stay in the ICU between the two groups. (6) Kaplan‒Meier survival analysis showed that the median survival time of patients with a serum sodium peak ≥154.9 mmol/L (group A) was significantly shorter than that of patients with a serum sodium peak < 154.9 mmol/L (group B) (16.7±1.4 d vs. 24.8±1.2 d, P <0.05). The serum sodium increase in elderly sepsis patients lasts for a long time, and the serum sodium fluctuation is relatively small. The serum sodium peak value has predictive value for 28-day mortality.

Lipid Accumulation Product and Cardiometabolic Index as Effective Tools for the Identification of Athletes at Risk for Metabolic Syndrome

Introduction: Metabolic syndrome (MS) is a growing global public health concern that is associated with increased risk for cardiovascular events, even in athletes. The lipid accumulation product (LAP) index and cardiometabolic index (CMI) have been shown to be efficient markers of MS in the general population; its applicability in athletes has not been discussed yet. We aimed to assess the role of LAP and CMI in predicting MS in athletes. Methods: We retrospectively enrolled 793 Olympic athletes practicing different sporting disciplines (power, skill, endurance, and mixed), classified arbitrarily into no risk (NR), low risk (LR), high risk (HR), or MS if they had 0, 1, 2, or 3 criteria for MS, respectively. Evaluations included a calculation of the LAP index, CMI, anthropometric measurements, and clinical and laboratorial variables. Results: Among our population, only 0.8% reached the criteria for MS, 9.1% were at HR for MS, 37.8% were defined as LR, and 52.3% had NR. Significant differences in anthropometric parameters and the principal components of MS criteria (blood pressure, lipidic profile, glycemia) were reported predominantly in HR athletes and those with MS (p &lt; 0.0001). LAP and CMI presented linearly increasing values from individuals with NR to those with MS (p &lt; 0.0001). In addition, HR and MS athletes were classified as “likely MS” (9.8%) and LR and NR athletes as “unlikely MS” (90.2%). After adjusting for potential confounders, LAP ≥ 34.66 and CMI ≥ 0.776 emerged as independent predictors for MS in the overall cohort (Hazar Ratio (HR) 7.22 [3.75–13.89], p &lt; 0.0001, and HR 5.37 [2.96–9.73], p &lt; 0.0001, respectively). The ROC curve revealed that these cut-offs in the general population predict MS with an area under the curve (AUC) of 0.80 and 0.79, respectively, for LAP and CMI. However, gender-related cut-offs seem to be more precise in predicting MS (LAP ≥ 38.79 for male, LAP ≥ 14.16 for female, and CMI ≥ 0.881 for male and ≥0.965 for female). Conclusion: The ROC curve analyses of LAP and CMI showed good diagnostic accuracy in predicting MS among athletes, despite the low prevalence of MS in our sample. Thus, these indexes may be used to promote screening for primary prevention and early detection of athletes at risk for MS to establish an early prevention strategy. Larger prospective studies are necessary to validate their benefit in the general population.

Development of a nomogram for predicting pancreatic portal hypertension in patients with acute pancreatitis: a retrospective study

IntroductionPancreatic portal hypertension (PPH) is a rare complication of acute pancreatitis (AP) that can lead to severe gastrointestinal bleeding. The risk factors associated with PPH, as well as the overall...